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UNCERTAINTY OF LIDARS IN COMPLEX TERRAIN

UNCERTAINTY OF LIDARS IN COMPLEX TERRAIN. Klaus Vogstad, Anne H Simonsen, Kyle Brennan and John Amund Lund Vienna, 06.02.2013. Uncertainty of lidars in complex terrain. Project goal. Quantify uncertainty of Lidars in complex terrain

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UNCERTAINTY OF LIDARS IN COMPLEX TERRAIN

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  1. UNCERTAINTY OF LIDARS IN COMPLEX TERRAIN Klaus Vogstad, Anne H Simonsen, Kyle Brennan and John Amund Lund Vienna, 06.02.2013

  2. Uncertaintyof lidars in complexterrain

  3. Project goal Quantifyuncertaintyof Lidars in complexterrain Determinethe most cost-effectiveconfigurationofmeasurementcampaigns (costvsquality) Test theusefullnessofLidars with variable scanninggeometries Uncertainty production estimate[%] Costefficiency Virtual measure-ments 25 20 Measure-ment masts 15 Combined Lidar and Masts 10 2.7 kEUR 100 kEUR 295 kEUR Costofmeasurementcampaign

  4. Project participants Sponsors

  5. Project overview

  6. TEST CAMPAIGN 50m mast • Phase I – Blind test at 80m mast: • 6 weeks • Phase II – Blind test at 50m mast: • 7 weeks • Parameters tested: • Wind speed • Wind direction • Turbulence • Uncertainties • Results with and without CFD correction • evaluated. • Wind data gradually released to • Consultants (50, then 80m) Overviewof Lidar systems

  7. Cfd correction Flow model solved using WindSim Sector resolution 5 degree Wind speed above boundary layer 10 m/s Terrain based on 5m height contours (5x5m grid resolution) Evaluation of lidar derived velocity by evaluating Vlos for every beam Derived velocity compared to cone center velocity (or mast center) for comparison Velocity scaling factor derived for each wind speed Derived wind speed is used as reference for scaling of lidar data Derived wind direction is used for sectorwise scaling of data

  8. Cfd correction • Scalingfactorsdiffersignificantly from sector to sector • Verycomplexterraingiveshighdependencyonboundarycondition in flowmodel • Small changes in wind speed can lead to largechanges in correctionfactors • Smoothing is applied to results to avoidlargeoscillations in correctionfactor • Neutralflowstratificationwasassumed for all winddirections • Large dependencyonorientation for 4-beam lidars Sectorwisecorrectionfactorsphase II Sectorwisecorrectionfactorsphase I

  9. Phase 1 Results – 80m – windcube lidar Comparison of wind speed at 80m - WINDCUBE Lidar against mast M4703. 6 weeks of data

  10. Phase 1 Results – 80m – Zephir lidar Comparison of wind speed at 80m – Zephir 300 Lidar against mast M4703 6 weeks of data

  11. Phase 1 Results – 80m – galion lidar Comparison of wind speed at 80m – Galion Long Range Lidar against mast M4703 6 weeks of data

  12. Phase 2 Results – 80m – windcube lidar Comparison of wind speed at 50m – WindCube v1 Lidar against mast M4704 7 weeks of data

  13. Phase 2 Results – 80m – zephir lidar Comparison of wind speed at 50m – Zephir 300 Lidar against mast M4704 7 weeks of data

  14. Phase 2 Results – 80m – galion lidar Comparison of wind speed at 50m – Galion Long Range Arc Scan from 450m against mast M4704. 7 weeks of data

  15. Summary of results

  16. Conclusion Uncertainty of Lidars is comparable to cup anemometry (2.5% ) in complex terrain, if we allow for CFD correction The Lidar use in complexterrainrequiresconfidence in CFD correction Data availabilitywaslow due to climatologicalconditionsofthesite (lowlevelclouds, low aerosols) and powersupply problems All systems performedwell, none were superior on all statisticalparameters From theexperienceofthiscampaign, werecommenduseof Lidars for measurementcampaignsin complexterrain The most cost-effectivemeasurementcampaign is probably a combination ofone mast (for LTC) and one Lidar for spatial coverage

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